Privacy-Enhanced Multimodal BERT Framework for Medical Imaging and Financial Analytics in the Cloud
DOI:
https://doi.org/10.15662/IJEETR.2025.0706009Keywords:
multimodal BERT, medical imaging, federated learning, differential privacy, cloud governance, financial analytics, HIPAA, homomorphic encryptionAbstract
We propose a privacy-enhanced, cloud-native framework that integrates a multimodal BERT-based architecture for medical imaging with robust data governance and financial analytics capabilities. The framework—PEM-BERT (Privacy-Enhanced Multimodal BERT)—combines image and text encoders to jointly learn from diagnostic images (X‑ray, CT, MRI) and associated clinical reports, leveraging contrastive pretraining and fine-tuning for downstream diagnostic tasks and billing/financial signal extraction. Privacy is addressed through a layered approach: (1) federated learning to keep raw imaging data on-premises, (2) differential privacy for gradient perturbation during model aggregation, (3) encrypted feature exchange using secure multiparty computation (SMPC) or homomorphic encryption for selective computations, and (4) strict provenance and consent metadata managed by cloud-native data governance services. The cloud deployment uses managed MLOps pipelines, role-based access control, and auditing to ensure regulatory compliance (HIPAA, GDPR) while enabling financial analytics such as cost-of-care estimation, claim risk scoring, and reimbursement optimization.
We evaluate PEM-BERT on a multi-institutional dataset simulated from publicly available imaging datasets and synthetic clinical notes, measuring diagnostic performance (AUC, F1), privacy leakage (membership inference, model inversion risk), and financial analytics accuracy relative to billing-ground truth. Results indicate that multimodal pretraining improves diagnostic AUC by 3–6% over image-only baselines, and that privacy mechanisms (federation + DP with ε=8) reduce membership inference risk by >60% while incurring a modest 1–3% drop in AUC. Cloud governance tooling provides end-to-end lineage and consent-tracking for >95% of data artifacts, enabling auditable model updates critical for reimbursement negotiations and payer audits.
We discuss trade-offs among privacy, model utility, and financial insight fidelity, and provide deployment guidance for healthcare organizations and cloud providers. The contributions include (a) a design for a privacy-anchored multimodal BERT for medical imaging, (b) an integrated governance and financial analytics layer suitable for cloud deployment, and (c) empirical evidence on utility-privacy trade-offs to inform hospital-payer collaborations.
References
1. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–318.
2. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
3. Reddy, B. V. S., & Sugumar, R. (2025, June). COVID19 segmentation in lung CT with improved precision using seed region growing scheme compared with level set. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020154). AIP Publishing LLC.
4. Konda, S. K. (2023). Strategic planning for large-scale facility modernization using EBO and DCE. International Journal of Artificial Intelligence in Engineering, 1(1), 1–11. https://doi.org/10.34218/IJAIE_01_01_001
5. Gonepally, S., Amuda, K. K., Kumbum, P. K., Adari, V. K., & Chunduru, V. K. (2023). Addressing supply chain administration challenges in the construction industry: A TOPSIS-based evaluation approach. Data Analytics and Artificial Intelligence, 3(1), 152–164.
6. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
7. Alsentzer, E., Murphy, J. R., Boag, W., Weng, W.-H., Jin, D., Naumann, T., & McDermott, M. (2019). Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323.
8. Alyasiri, S., Rana, O., & Krishna, C. (2020). Encrypted machine learning: A survey. Journal of Cloud Computing, 9(1), 1–29.
9. Sardana, A., Kotapati, V. B. R., & Ponnoju, S. C. (2025). Autonomous Audit Agents for PCI DSS 5.0: A Reinforcement Learning Approach. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 4(1), 130-136.
10. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations (SimCLR). Proceedings of the 37th International Conference on Machine Learning, PMLR 119:1597–1607.
11. KESAVAN, E. (2025). THE FUTURE OF WORK: TRENDS AND IMPLICATIONS FOR MANAGEMENT. i-Manager's Journal on Management, 19(4).
12. Manda, P. (2023). A Comprehensive Guide to Migrating Oracle Databases to the Cloud: Ensuring Minimal Downtime, Maximizing Performance, and Overcoming Common Challenges. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8201-8209.
13. Thambireddy, S., Bussu, V. R. R., & Joyce, S. (2023). Strategic Frameworks for Migrating Sap S/4HANA To Azure: Addressing Hostname Constraints, Infrastructure Diversity, And Deployment Scenarios Across Hybrid and Multi-Architecture Landscapes. Journal ID, 9471, 1297. https://www.researchgate.net/publication/396446597_Strategic_Frameworks_for_Migrating_Sap_S4HANA_To_Azure_Addressing_Hostname_Constraints_Infrastructure_Diversity_And_Deployment_Scenarios_Across_Hybrid_and_Multi-Architecture_Landscapes
14. Shashank, P. S. R. B., Anand, L., & Pitchai, R. (2024, December). MobileViT: A Hybrid Deep Learning Model for Efficient Brain Tumor Detection and Segmentation. In 2024 International Conference on Progressive Innovations in Intelligent Systems and Data Science (ICPIDS) (pp. 157-161). IEEE.
15. Kumar, R., Christadoss, J., & Soni, V. K. (2024). Generative AI for Synthetic Enterprise Data Lakes: Enhancing Governance and Data Privacy. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 7(01), 351-366.
16. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. NAACL-HLT, 4171–4186.
17. Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. International Conference on Learning Representations (ICLR).
18. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 1273–1282.
19. Kandula, N. (2024). Optimizing Power Efficient Computer Architecture With A PROMETHEE Based Analytical Framework. J Comp Sci Appl Inform Technol, 9(2), 1-9.
20. Nguyen, P. A., Nguyen, P., Hawkins, R., & Haug, P. J. (2018). Predicting hospital length of stay and billing from clinical notes and admissions data. Journal of Biomedical Informatics, 84, 52–62.
21. Sivaraju, P. S. (2024). PRIVATE CLOUD DATABASE CONSOLIDATION IN FINANCIAL SERVICES: A CASE STUDY OF DEUTSCHE BANK APAC MIGRATION. ITEGAM-Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA).
22. Radford, A., Kim, J. W., Hallacy, C., et al. (2021). Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8748–8763.
23. Gosangi, S. R. (2023). Reimagining Government Financial Systems: A Scalable ERP Upgrade Strategy for Modern Public Sector Needs. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8001-8005.
24. Anbalagan, B., & Pasumarthi, A. (2022). Building Enterprise Resilience through Preventive Failover: A Real-World Case Study in Sustaining Critical Sap Workloads. International Journal of Computer Technology and Electronics Communication, 5(4), 5423-5441.
25. Adari, Vijay Kumar, “Interoperability and Data Modernization: Building a Connected Banking Ecosystem,” International Journal of Computer Engineering and Technology (IJCET), vol. 15, no. 6, pp.653-662, Nov-Dec 2024. DOI:https://doi.org/10.5281/zenodo.14219429.
26. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
27. Reddy, B. T. K., & Sugumar, R. (2025, June). Effective forest fire detection by UAV image using Resnet 50 compared over Google Net. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020274). AIP Publishing LLC.
28. Kakulavaram, S. R. (2023). Performance Measurement of Test Management Roles in ‘A’ Group through the TOPSIS Strategy. International Journal of Artificial intelligence and Machine Learning, 1(3), 276. https://doi.org/10.55124/jaim.v1i3.276
29. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.
30. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234–241.
